{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "np.random.seed(4873)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('./data/simple_example.csv')\n",
    "Y = data[['y']]\n",
    "X = data[['x']]\n",
    "# 加入新的随机变量，此变量的系数应为0\n",
    "X['z'] = np.random.randint(2, size=20)\n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ln_alphas = np.linspace(-6, -1, 100)\n",
    "coefs = []\n",
    "for ln_alpha in ln_alphas:\n",
    "    # lasso模型的惩罚项并不包括截距项，\n",
    "    # 我们需要在数据中手动添加截距项变量const\n",
    "    model = linear_model.Lasso(alpha=np.exp(ln_alpha), fit_intercept=False)\n",
    "    model.fit(X[['x', 'z', 'const']], Y)\n",
    "    coefs.append(model.coef_.tolist())\n",
    "coefs = np.array(coefs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为在Matplotlib中显示中文，设置特殊字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams.update({'font.size': 13})\n",
    "# 创建一个图形框\n",
    "fig = plt.figure(figsize=(6, 6), dpi=100)\n",
    "# 在图形框里只画一幅图\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(ln_alphas, coefs[:, 0], 'b-.', label='x的参数a')\n",
    "ax.plot(ln_alphas, coefs[:, 1], 'r:', label='z的参数b')\n",
    "ax.plot(ln_alphas, coefs[:, 2], 'g', label='const的参数c')\n",
    "# 设置图例的样式\n",
    "legend = plt.legend(loc=4, shadow=True)\n",
    "legend.get_frame().set_facecolor(\"#6F93AE\")\n",
    "ax.set_yticks(np.arange(-1.0, 1.3, 0.2))\n",
    "ax.set_xlabel(\"$\\ln(ln\\_alpha)$\")\n",
    "plt.savefig('linear_ml_reg.png', dpi=200)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
